“If you can’t measure it, you can’t manage it”
is often miss-attributed to Deming (1993Deming, W Edwards. 1993. “The New Economics for Industry, Government, Education.” MIT Press.),
who was actually trying to
make the opposite point (Berenson 2016Berenson, Robert A. 2016. “If You Can’t Measure Performance, Can You Improve It?”JAMA 315 (7): 645–46.).
Regardless,
service level indicators
are an important part of
researching,
managing
and seeking to improve transit operations
(Fielding 1987Fielding, Gordon J. 1987. Managing public transit strategically: a comprehensive approach to strengthening service and monitoring performance. 1st ed. Jossey-Bass Public Administration Series. San Francisco: Jossey-Bass Publishers.; Ryus et al. 2003Ryus, Paul, M Connor, S Corbett, A Rodenstein, L Wargelin, L Ferreira, Y Nakanishi, and K Blume. 2003. “TCRP Report 88: A Guidebook for Developing a Transit Performance-Measurement System.” Transit Cooperative Research Program.).
A wide range of indicators already exist.
Examples include:
those in the Transit Capacity and Quality of Service Manual (TCQSM)(Kittleson & Associates et al. 2013Kittleson & Associates, Parsons Brinckerhoff, KFH Group, Texas A&M Transportation Institute, and ARUP. 2013. Transit Capacity and Quality of Service Manual, Third Edition. Third Edition, TCRP Report 165. Washington DC: Transportation Research Board; Transportation Research Board. http://www.trb.org/Main/Blurbs/169437.aspx.),
the Transit Score metric (Walk Score 2023Walk Score. 2023. “Transit Score Methodology.”https://www.walkscore.com/transit-score-methodology.shtml.)
and many more.
Practitioners,
researchers
and advocates
seeking to use such metrics
may face two inter-related challenges:
(1)
there is the problem of calculating the metrics themselves
for a specific location
and service pattern; and
(2)
is the challenge of explaining the metrics,
their meaning
and importance
to those who are not specialists in transit,
such as to politicians
or the general public1
Of the examples above,
the metrics in the TCQSM appear:
difficult to calculate in practice,
and difficult to explain
because there are a multitude of indicators
(although there is an entire guidebook explaining them, which might help somewhat);
while,
in contrast,
Transit Scores can be obtained
simply by typing an address into a website,
but cannot be calculated independently
and lack a detailed
(and open source)
description of the methodolgy / algorithm..
However,
a relatively simple transit Supply Index (SI)
has been previous developed (Currie and Senbergs 2007Currie, Graham, and Zed Senbergs. 2007. “Identifying spatial gaps in public transport provision for socially disadvantaged Australians: the Melbourne needs-gap study.” Australasian Transport Research Forum.),
This reduces service levels to a single score
(like the Transit Score),
but is open
and can be independently calculated by anyone.
It is obtained by calculating
the number of transit arrivals
at stops within an area of interest,
with an adjustment made to account for the typical walk-access catchment for each stop.
Hence,
higher SI scores indicate
areas with higher frequency
and/or better coverage.
Unfortunately,
the SI does not appear to have been widely used,
perhaps in part because at the time it was first published
timetable data was not typically publicly available
in a standardized
and machine-readable format.
The scores reported in Currie and Senbergs (2007)
has been calculated directly from a database of services
provided by the transit authority
in Melbourne, Australia.
However, this database appears to have been
in a format specific to Victoria
and the agency itself2
Public Transport Victoria (PTV),
and so if SI scores for a different location were desired3 Or perhaps even for Melbourne again,
but at a different time
given that the database format has likely changed
and the Currie and Senbergs (2007) analysis algorithms are not readily available.
considerable work would be required to wrangle the data and calculate scores.
However,
since the widespread adoption
of the General Transit Feed Specification (GTFS),
timetable data
and tools for processing it
are now much more widely available
than they were in 2007.
More than 10,000 agencies
are now providing GTFS feeds4 There are two forms:
GTFS-static consisting of the timetable data (the scheduled services);
and GTFS-realtime, which includes vehicle arrivals and departure times based on real-world position data.
This paper and project uses only the GTFS-static (timetable) format.(MobilityData undatedMobilityData. undated. General Transit Feed Specification (GTFS). {https://gtfs.org/}.),
suggesting that the development of code
to calculate SI scores directly from GTFS
might allow it to be used more widely
in research and practice.
Previous work undertaken
by Monash University’s Public Transport Research Group (PRTG),
available on Github (Reynolds 2023Reynolds, James. 2023. “Transit_supply_index_GTFS.”https://github.com/James-Reynolds/Transit_Supply_Index_GTFS.),
developed R functions to calculate SI scores
from the Victorian PTV GTFS feed.
However,
the code was inefficient5
To calculate SI scores for the Victorian GTFS feed
would take the original code base approximately as long as it the service delivery itself took
i.e. (SI scores fora whole week of transit service would take a whole week to compute!).
and untidy,
and only allowed scores to be calculated for a whole day of transit service.
More recently,
PTRG has commenced a project which requires SI scores by hour of the day.
Unfortunately,
the current code does not have this capability,
and major changes will be needed to calculate stop arrivals on an hourly, rather than daily, basis.
However,
this also provides an opportunity to revise and improve the code
so that it is more readily accessible,
usable and
understandable for others,
and quicker.
This document reports
the development of code to calculate SI scores
from GTFS datasets
as an R package (Reynolds 2024Reynolds, James. 2024. “Gtfssupplyindex.”https://github.com/James-Reynolds/gtfssupplyindex.).
Structuring the code as an R package6
Rather than as scripts within a R Markdown file,
as for the original Transit_Supply_Index_GTFS efforts
provides various advantages as far as
adhering to standardised conventions,
accessibility and sharing,
testing
and so on (Wickham and Bryan 2023Wickham, Hadley, and Jennifer Bryan. 2023. R Packages. " O’Reilly Media, Inc.". https://r-pkgs.org/.).
More broadly,
the motivation for this research is
to better understand how GTFS data might be used
to produce benchmarking metrics that can be calculated using open-source code.
Such metrics might then be able to be used
to assess proposed network changes
or other analyses
without the need for specialist software
or bespoke calculation7
More formally,
the (null) hypothesis tested in this research is
that the Supply Index cannot be calculated directly from a GTFS feed..
A related objective is
to increase the availability of metrics that are relatively easy to understand and use
when making decisions about
or advocating for changes to existing services,
including for those who may not
be technical specialists
in transit planning and scheduling.
The rest of this document
is structured as follows:
the next section discusses the research context
of transit metrics
and the the Supply Index.
In the third section
the methodology adopted for the code development is outlined,
including discussion of the case studies
(GTFS feeds)
used to test and verify the code output.
In the fourth section
results are presented,
including SI scores for SA1s across Greater Melbourne on an hour-by-hour basis.
Results are then discussed,
followed by a brief conclusion
that includes the identification of directions for future research.
Research context
Even a brief search shows
that there is a very large number of metrics
available for benchmarking transit services.
Examples include:
(1) those in the Transit Cooperative Research Program (TCRP) Report 88, which is an extensive guidebook on developing a performance-measurement system (Ryus et al. 2003Ryus, Paul, M Connor, S Corbett, A Rodenstein, L Wargelin, L Ferreira, Y Nakanishi, and K Blume. 2003. “TCRP Report 88: A Guidebook for Developing a Transit Performance-Measurement System.” Transit Cooperative Research Program.);
(2) online databases provided by
the Florida Transit Information System (FTIS) (Florida Transit Information System 2018Florida Transit Information System. 2018. “Urban Integrated National Transit Database.”http://www.ftis.org/urban_intd.aspx.)
and International Association of Public Transport (UITP) (2015International Association of Public Transport (UITP). 2015. “Mobility in Cities Database 2015.” Brussels, Belgium. uitp.org/publications/mobility-in-cities-database/.);
(3) those used in the extensive annual benchmarking programme
undertaken yearly by the Transport Strategy Centre,
which includes over 100 transit providers around the world (Imperial College London undatedImperial College London. undated. “Transport Strategy Centre (TSC); Applied Research.” undated. https://www.imperial.ac.uk/transport-engineering/transport-strategy-centre/applied-research/.); and
(4) a recently developed methodology to calculate ‘blank spots’ within an area,
being those places beyond 400/800 metre walking distances to/from bus and tram stops/train stations (Alamri et al. 2023Alamri, Sultan, Kiki Adhinugraha, Nasser Allheeib, and David Taniar. 2023. “GIS Analysis of Adequate Accessibility to Public Transportation in Metropolitan Areas.”ISPRS International Journal of Geo-Information 12 (5): 180.).
The Fielding Triangle (Fielding 1987Fielding, Gordon J. 1987. Managing public transit strategically: a comprehensive approach to strengthening service and monitoring performance. 1st ed. Jossey-Bass Public Administration Series. San Francisco: Jossey-Bass Publishers.) provides a framework
for understanding how such metrics combine
service inputs,
service outputs
and service consumption
to describe cost efficiency,
cost effectiveness
or service effectiveness.
At a larger scale,
Litman (2003Litman, Todd. 2003. “Measuring Transportation: Traffic, Mobility and Accessibility.” 10. Vol. 73. Institute of Transportation Engineers. ITE Journal. Washington, D.C.: Institute of Transportation Engineers.)
and Litman (2016Litman, Todd. 2016. “When Are Bus Lanes Warranted? Considering Economic Efficiency, Social Equity and Strategic Planning Goals.” Victoria Transport Policy Institute. http://www.vtpi.org/blw.pdf.)
discuss some of the traffic,
mobility,
accessibility,
social equity,
strategic planning
and other rational decision-making frames
that might underlie such transit metrics,
while Reynolds et al. (2017Reynolds, James, Graham Currie, Geoff Rose, and Alistair Cumming. 2017. “Moving Beyond Techno-Rationalism: New Models of Transit Priority Implementation.” In Australasian Transport Research Forum 2017. Auckland, New Zealand.) extends this into models of how
institutionalism,
incrementalism
and other public policy models
might apply to decision-making processes.
Further examples include:
(1) Guzman, Oviedo, and Rivera (2017Guzman, Luis A., Daniel Oviedo, and Carlos Rivera. 2017. “Assessing Equity in Transport Accessibility to Work and Study: The Bogotá Region.”Journal of Transport Geography 58: 236–46.),
who develop a measure of accessibility
in the context of policy development
and social equity
for Latin American Bus Rapid Transit (BRT) based networks; and
(2) the street space allocation metrics
based around 10 ethical principles
introduced by Creutzig et al. (2020Creutzig, Felix, Aneeque Javaid, Zakia Soomauroo, Steffen Lohrey, Nikola Milojevic-Dupont, Anjali Ramakrishnan, Mahendra Sethi, et al. 2020. “Fair Street Space Allocation: Ethical Principles and Empirical Insights.”Transport Reviews 40 (6): 711–33. https://doi.org/10.1080/01441647.2020.1762795.).
However,
many of these metrics appear difficult to calculate,
complex to explain or understand,
and likely not well suited to communication
with those who are not transit planners or engineers, or otherwise technical specialists.
Where pre-calculated metrics
are immediately available
it may not be possible for practitioners,
researchers
or advocates
to independently generate metrics for proposed system changes
or to even know exactly how scores for the existing services levels are calculated.
For example,
Transit Scores
for locations with a published GTFS feed
are readily available on a website,
eliminating the need for any calculations.
The meaning of these Transit Scores
appears easy to explain,
as the highest possible score of 100
represents what might be experienced in the centre of New York(Walk Score 2023Walk Score. 2023. “Transit Score Methodology.”https://www.walkscore.com/transit-score-methodology.shtml.)].
However,
the Transit Score algorithm
is patented
and effectively a black box.
It is not possible to calculate scores independently
or understand how the metric might change
with alteration to the transit system or services,
or the surrounding environment.
Transit Score, therefore,
fails the first of the aforementioned challenges,
as practitioners, researchers and advocates
can only use those scores provided by Walk Score (2023Walk Score. 2023. “Transit Score Methodology.”https://www.walkscore.com/transit-score-methodology.shtml.)
While the metric is simple to explain,
as the closer to 100, the better,
because it is based on
a patented algorithm it may not be easy to understand or explain
the connection between real-world conditions
and the Transit Score,
or what might need to be done
to improve the score and service levels.
Nor does it appear to be possible
for Transit Scores to be generated for proposed changes to networks.
Another example is the TCQSM,
which specifies Levels of Service (LOS) between A and F
across a range of factors8
Including service span,
frequency,
speed,
the proportion of the population serviced,
competitiveness of travel times to car-based travel,
and many more..
This scoring scheme appears relatively simple to explain9
A is good and F is bad.
Also this scoring system matches the A to F LOS scoring used in many traffic capacity analysis software and manuals.,
and the detail within Kittleson & Associates et al. (2013Kittleson & Associates, Parsons Brinckerhoff, KFH Group, Texas A&M Transportation Institute, and ARUP. 2013. Transit Capacity and Quality of Service Manual, Third Edition. Third Edition, TCRP Report 165. Washington DC: Transportation Research Board; Transportation Research Board. http://www.trb.org/Main/Blurbs/169437.aspx.)
provides a resource for anyone wanting to better understand what the scores mean.
However,
calculation of many of TCQSM metrics may need specialised software
and datasets10
For example,
the Service Coverage Area metric in the TCQSM (pp. 5-8 to 5-21)
may require GIS or other analysis,
on top of accurate data about population densities,
stop locations
and service schedules.
and it might be challenging to explain
the detail of these measures
or how to improve them
to non-technical decision-makers,
stakeholders
or others involved in transit management or advocacy.
The introduction of the General Transit Feed Specification (GTFS)
and widespread release of schedule data in this format,
however,
has helped towards making transit metrics more broadly available and useable.
GTFS is an open,
text-based format
that was developed originally to allow transit information to be included in the Google Maps navigation platform (MobilityData undatedMobilityData. undated. General Transit Feed Specification (GTFS). {https://gtfs.org/}.).
The data structure is shown in the below figure.
GTFS entity relationship diagram. Source: adapted by author from Alamri et al (2023) and the GTFS Schedule Reference (16/11/2023 revision).
In the Entity Relationship Diargram (ERD) shown above, each box represents a database table in the GTFS, with table rows indicating the variables (columns) included in each11 For example, each record in the ‘stops’ table includes a value for stop_id, stop_name, stop_lat and stop_lon.. Relationships between the tables are indicated by the connecting lines, and Primary Key (PK) and Foreign Key (FK) designations12 For example, stop_id also appears in the ‘stop_times’ table as a Primary Key and Foreign Key.. `Crow’s feet’ indicate the relationships between each table13 See https://i.stack.imgur.com/fxaAq.png for guide to the symbols. But, for example, the stops table is required, with the stop_id field providing a unique (primary) key for every stop. Within the stop_times table (which is also required) the stop_id field is a foreign key. Each unique stop_id can appear many times in the stop_times table, but must appear only once in the stops table. In the stop_times table each combination of trip_id, stop_id and arrival time must be unique (But, see note 2!) meaning that these fields represent a composite key..
GTFS now provides a mechanism
for including individual transit systems
in many online products and analyseses,
including the Transit Score metric itself.
Wong (2013Wong, James. 2013. “Leveraging the General Transit Feed Specification for Efficient Transit Analysis.”Transportation Research Record 1 (2338): 11–19. https://doi.org/10.3141/2338-02.) provides another example of what can be done with GTFS data,
having developed code to calculate of some of the TCQSM metrics14
Daily average headways,
route length
and stop numbers
for 50 transit operators..
While the Wong (2013Wong, James. 2013. “Leveraging the General Transit Feed Specification for Efficient Transit Analysis.”Transportation Research Record 1 (2338): 11–19. https://doi.org/10.3141/2338-02.) open-source code is readily available15https://github.com/jcwong86/GTFS_Explore_Tool
this is now 11 years old
and does not appear to be currently maintained.
Future research may involve
reviewing this code
and using it to analyse modern GTFS feeds.
However,
in this paper the aim is more modest,
being to use GTFS data to calculate Currie and Senbergs’ (2007) SI.
The Supply Index (SI) equation
is shown in the margin figure16
Minor adjustments have been made
to generalise the equation,
as Currie and Senbergs (2007) focus
was the context of Melbourne’s Census Collection Districts (CCD)
and calculations based on a week of transit service.
CCDs predate the introduction of
Statistical Areas 1, 2, 3, and 4 (SA1, SA2, SA3, SA4),
and other geographical divisions
currently used by the Australian Bureau of Statistics (ABS),
which may be more familiar to readers.,
in which:
(1) \(SI_{area, time}\) is the Supply Index for the area of interest
and a given period of time;
(2) \(Area_{Bn}\) is the buffer area for each stop (n) within the area of interest.
In Currie and Senbergs (2007) this was based on
a radius of 400 metres for bus and tram stops,
and 800 metres for railway stations;
(3) \(Area_{area}\) is the area of the area of interest; and
(4) \(SL_{n,time}\) is the number of transit arrivals for each stop
for a given time period.
An advantage of the SI is
that it is a relatively simple number
to calculate,
understand
and explain.
It describes
the number of transit arrivals
at stops within
an area of interest
and time frame,
multiplied by a factor
accounting for the proportion of the area of interest that is within typical walking distance of each stop.
Hence,
more services,
more stops
and higher frequencies
would all increase the SI score.
The SI does not incorporate
service span,
speed
or other elements of a transit service.
These may be important
to passenger experience,
but might add considerable complexity.
Simplicity is also helped
by the way
that the Index is additive,
in that \(SI_{area, time}\) scores can be aggregated
to calculate an overall score
across multiple time periods
or for a region encompassing multiple areas of interest.
Methodology
R (R Core Team 2023R Core Team. 2023. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.), a widely used and readily available statistical programming language, was adopted for code development. The package development setup and workflow described by Wickham and Bryan (2023Wickham, Hadley, and Jennifer Bryan. 2023. R Packages. " O’Reilly Media, Inc.". https://r-pkgs.org/.)
was adopted in this study.
Various existing packages were relied upon including: the sf package (Pebesma 2023Pebesma, Edzer. 2023. Sf: Simple Features for r. https://r-spatial.github.io/sf/.) for geospatial analysis; the tidyverse (Wickham et al. 2019Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the tidyverse.”Journal of Open Source Software 4 (43): 1686. https://doi.org/10.21105/joss.01686.); gtfstools (Daniel Herszenhut et al. 2022Herszenhut, Daniel, Rafael H. M. Pereira, Pedro R. Andrade, and Joao Bazzo. 2022. Gtfstools: General Transit Feed Specification (GTFS) Editing and Analysing Tools. https://ipeagit.github.io/gtfstools/.); and tidytransit (Poletti et al. 2023Poletti, Flavio, Daniel Herszenhut, Mark Padgham, Tom Buckley, and Danton Noriega-Goodwin. 2023. Tidytransit: Read, Validate, Analyze, and Map GTFS Feeds. https://github.com/r-transit/tidytransit.). Some code was adapted from the tidytransit, gtfstools and other package’s examples, vignettes and other documentation.
Tidytransit is licensed under GPL-2 | GPL-3,
while gtfstools is lincensed under the more permissive MIT license.
For simplicity,
the gtfssupplyindex package developed here
is licensed under GPL-3
so as to match tidytransit.
Cases
Mornington Penninsula SA1 zones and location of Mornington Tourist Rail stops.
Code development was undertaken using a number of cases for testing purposes, being: the Mornington Penninsula Tourist Railway, the New York subway, and Melbourne.
The Morning Penninsula Tourist Railway runs on Sundays and Wednesdays between Moorooduc and Mornington, with an intermediate stop at Tanti Park17https://transitfeeds.com/p/mornington-railway/806/latest/stops. A GTFS feed from 2018 was selected. ABS data was also used,
primarily through the strayr
and absmapsdata packages (Mackey et al. 2023Mackey, Will, Matt Johnson, David Diviny, Matt Cowgill, Bryce Roney, William Lai, and Benjamin Wee. 2023. “Strayr.”https://runapp-aus.github.io/strayr/.).
The Mornington Peninsular SA3 zone and the SA1 zones contained within were adopted as the areas_of_interest.
Tidytransit includes a sample GTFS feed from New York’s MTA
(including the subway!),
and so this was used for code tests were appropriate.
Larger scale testing was performed using the Victorian GTFS feed,
published by Public Transport Victoria (PTV),
sourced via Transit Mobility Data, (2023Transit Mobility Data,. 2023. “PTV GTFS - OpenMobilityData.” 2023. https://transitfeeds.com/p/ptv/497.) for historical feeds.
Again, ABS data was used for the areas_of_interest.
Results
Code structure and output
Developed code is available and documented on github (Reynolds 2024Reynolds, James. 2024. “Gtfssupplyindex.”https://github.com/James-Reynolds/gtfssupplyindex.).
The structure and functions used to generate each table are shown in the below Entity Relationship Diagram (ERD).
Entity Relationship Diagram (ERD) showing the data structure and functions
The package takes input from three files:
a gtfs feed (gtfs.zip)
a sf object describing the geometry of the areas for which the SI is to be calculated
a csv file defining the buffer zone distances (in metres) for each route_type - a version of this file is included in the package.
It outputs the si_by_area_and_hour table (bottom right), which reports the SI score for each hour of the day across dates specified by the user.
Mornington Penninsula Tourist Railway hourly SI values for December 30, 2018, for SA1 zones
area_id
10
11
12
13
14
15
214021381
0.0000672
0.0522962
0.0523635
0.0000672
0.0522962
0.0523635
214021385
0.0000000
0.0067970
0.0067970
0.0000000
0.0067970
0.0067970
214021591
0.2436873
0.1366432
0.3803305
0.1366432
0.2436873
0.3803305
214021592
0.0684965
0.0000000
0.0684965
0.0000000
0.0684965
0.0684965
Functions
This section briefly describes each of the functions shown in the above ERD, and how these facilitate the calculation of the SI scores.
gtfs_by_route_type function
Entity Relationship Diagram (ERD) excerpt, gtfs_by_route_type function
GTFS data is first loaded, with the gtfs_by_route_type function splitting this into a list (by route_type) of tidygtfs objects. This is achieved using the filter_by_route_type function from the gtfstools package (Danile Herszenhut et al. undatedHerszenhut, Danile, Rafael H. M. Pereira, Pedro R. Andrade, and Joao Bazzo. undated. Gtfstools; Filter GTFS Object by Route Type (Transport Mode). https://ipeagit.github.io/gtfstools/reference/filter_by_route_type.html.).
#load the revised mornington GTFS data
list_gtfs = gtfssupplyindex:::gtfs_by_route_type(system.file(
"extdata/mornington180109",
"gtfs.zip",
package = "gtfssupplyindex",
mustWork = TRUE))
names(list_gtfs) %>% kable(caption = "tidygtfs objects within list_gtfs")
tidygtfs objects within list_gtfs
x
rail
names(list_gtfs[[1]]) %>% kable(caption = "tidygtfs structure of first element of list_gtfs")
tidygtfs structure of first element of list_gtfs
x
agency
calendar
calendar_dates
fare_attributes
fare_rules
feed_info
routes
shapes
stop_times
stops
trips
.
load_areas_of_interest function
Entity Relationship Diagram (ERD) excerpt, load_areas_of_interest function
Geographical data about the areas of interest are first loaded by the load_areas_of_interest.R function into an sf object using the Pebesma (2023Pebesma, Edzer. 2023. Sf: Simple Features for r. https://r-spatial.github.io/sf/.) package. The resultant areas_of_interest table contains each area_id and its associated geometry.
## Reading layer `mornington_sa12021' from data source
## `C:\Users\rjulian\Documents\gtfssupplyindex\inst\extdata\mornington_sa12021.geojson'
## using driver `GeoJSON'
## Simple feature collection with 392 features and 1 field
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 144.6514 ymin: -38.49738 xmax: 145.2615 ymax: -38.16239
## Geodetic CRS: WGS 84
First 6 entries in areas_of_interest table
area_id
geometry
21402137701
MULTIPOLYGON (((144.9617 -3…
21402137702
MULTIPOLYGON (((144.9916 -3…
21402137703
MULTIPOLYGON (((144.9923 -3…
21402137704
MULTIPOLYGON (((144.9878 -3…
21402137705
MULTIPOLYGON (((145.0015 -3…
21402137706
MULTIPOLYGON (((145.0088 -3…
Data about buffer zones, specifically the walking distance threshold assigned to each route_type (mode) is then loaded, again through a function (load_buffer_zone.R). The package includes this information in a csv file, in which it is assumed that the buffer zone is definied in metres.
First six entries in buffer_distance definitions
route_type
buffer_distance
short_name
0
400
lrt
1
800
subway
2
800
rail
3
400
bus
4
800
ferry
5
400
cable_tram
stops_in_walk_dist function
Entity Relationship Diagram (ERD) excerpt, stops_in_walk_dist function
The stops_in_walk_dist function then generates a list (by route_type) in which each element is a datea table describing which stops are within walking distance of which areas of interest. Each table entry includes a stop_id, an area_id that is within the buffer_distance threshold of that stop, and the corresponding area related terms in the SI calculation18 This is the \(Area_{Bn} / Area_{Area}\) bit.
‘Rail’ element of the stops_in_or_near_areas list for the Mornington Pennisula datasets, first six entries
stop_id
area_id
area_terms
1388695887
21402159101
0.7999912
1388695887
21402159102
0.0168220
1388695887
21402159105
0.6779951
1388695887
21402159107
0.6453927
1388695887
21402159111
0.2011127
1388695887
21402159113
0.1081424
The variables passed to the stop_in_or_near_areas function are list_gtfs19 The list of tidygtfs objects ouput by the gtfs_by_route_type function, the areas_of_interest table20 Which is the output of the load_areas_of_interest function, the buffer_distance table21 Output by the load_buffer_zones function, and an EPSG_for_transform variable. This last variable is the Coordinate Reference System (CRS) value relevant to the geographic location. It is used to project the latitude and longitude values included in the GTFS data and area_of_interest data into metres, in this case relating to the GDA94 / MGA zone 55 relevant to Australia22https://epsg.io/28355.
The stops_in_walk_dist function is complicated by the need to have different buffer distances for each route_type, and to only include those parts of the walking catchment that are within each area of interest. It is structured as follows:
The first part of the stops_in_walk_dist function looks up the buffer_distance_length specific to each route_type in the list (by route_type) of tidygtfs objects. This value is appended to each tidygtfs object as an additional element (named buffer_distance).
Next the areas_of interest are transformed from latitude and longitude into metres, and the area is calculated.
The stops_in_walking_distance function then applies a sub-function, stops_in_walk_dist_one_route, to the list_gtfs (list of tidygtfs objects), in which:
A list of stops is extracted from the tidygtfs, using the stops_as_sf function from Poletti et al. (2023Poletti, Flavio, Daniel Herszenhut, Mark Padgham, Tom Buckley, and Danton Noriega-Goodwin. 2023. Tidytransit: Read, Validate, Analyze, and Map GTFS Feeds. https://github.com/r-transit/tidytransit.).
Circles are then drawn around each stop, with the radius equal to the buffer distance. Intersecting these circles with the areas_of_interest then outputs an sf showing the catchment of each stop within each area of interest
Finally, the \(area_{Bn}\) terms are calculated for each combination of stop_id and area_id,
The stops_in_walk_dist function includes a mode with output printed at each intermediate step (verbose = TRUE). The below shows the Morington Penninsula railway calculations again (as per the previous table), but run with verbose = TRUE so that intermediate steps are printed.
Step 5, circles around each stop, showing the intersections with the areas_of_interest, all stops
as above, but from each stop individually
Calculating stop arrivals and the SI, SI_total and hourly
Entity Relationship Diagram (ERD) excerpt, si_calc, si_total and hourly functions
The si_calc.R function takes the stops_in_or_near_areas list (by route_type) of dataframes and the list_gtfs list (by route_type) of tidygtfs objects and calculates the si_by_route_type23 Which, again, is a list (by route_type) of dataframes, each containing a record for each area_id and combination of date, start_time and end_time specified by the user, and the corresponding (calculated) SI value.
The tidytransit package includes an article about generating a departure timetable from a gtfs dataset (Poletti undatedPoletti, Flavio. undated. Tidytransit: Generate a Departure Timetable. https://r-transit.github.io/tidytransit/articles/timetable.html.). Code from that article was adapted from this example to create functions to calculate the number of departures and arrivals from (a vector of) stop_ids within a given time period. These functions are included in the package24 See departures_and_arrivals.R and called by the si_calc function25 Although this is not shown on the ERD. Output from the arrivals function is shown in the bellow table.
Arrivals at Mornington Station (stop_id 1388695887) for 30/12/2018
All the inputs to calculate the si are now available. Hence, the SI.calc function can be run, resulting in the si_by_route_type list (by route_type) of tables showing the area_id corresponding SI values
SI values for Mornington Penninsula Railway services on 30/12/2018 (full day)
area_id
SI
214021381
0.2094539
214021385
0.0271880
214021591
1.5213219
214021592
0.2739859
The si_total function aggregates the si_by_route_type tables so that values are no longer separated by mode. Although in the case of the Mornington Penninsula Railway there is only one route_type.
Finally, the hourly function runs the si_calc and si_total functions for every hour in a single day. It outputs a table showing the SI scores for each area for each hour of the day27 Across the service span. The below table shows this output, together with row and column totals.
Mornington Penninsula Tourist Railway hourly SI values for December 30, 2018, for SA1 zones
area_id
10
11
12
13
14
15
Total
214021381
0.0000672
0.0522962
0.0523635
0.0000672
0.0522962
0.0523635
0.2094539
214021385
0.0000000
0.0067970
0.0067970
0.0000000
0.0067970
0.0067970
0.0271880
214021591
0.2436873
0.1366432
0.3803305
0.1366432
0.2436873
0.3803305
1.5213219
214021592
0.0684965
0.0000000
0.0684965
0.0000000
0.0684965
0.0684965
0.2739859
Total
0.3122510
0.1957364
0.5079874
0.1367104
0.3712770
0.5079874
2.0319496
Testing the package on the Mornington Peninsula Tourist Railway and (just) the Mornington Peninsula SA3 zone
In the previous sections the code structure, output and functions were described. Results the Mornington Peninsula Tourist Railway’s GTFS data set and the SA1 zones were also presented as an example case.
In this section the same GTFS data is used, but with the areas of interest set instead to be the entire Mornington Peninsula SA3 area. As all three of the Mornington Peninsula Raiway’s stops are located within this SA3 zone the calculation of the SI scores by hand is relatively simple, and is used here as a check on the results.
Mornington Penninsula Tourist Railway catchments within the Mornington Penninsula SA3 zone
Mornington Penninsula Tourist Railway catchments within the Mornington Penninsula SA3 zone
kable(stops_in_or_near_areas, caption = "Catchment of each stop within Mornington Penninsula SA3 zone")
Catchment of each stop within Mornington Penninsula SA3 zone
stop_id
area_id
area_terms
1388695887
21402
0.0027748
1452182324
21402
0.0027748
650916735
21402
0.0027748
As shown in the figures and above tables, the area terms for each of the combinations of stop_id and area_id are ~ 0.0028. All of the catchment areas of each stop fall within the single area_of_interest, being the Mornington Penninsula SA328 area_id 21402.
Checking the area terms calculation is relatively trivial for this case, as the ABS maps data comes with an areasqkm_2021 field. Dividing the catchment area of the stop29\(Area_{Bn} = \pi0.8^2\) by the area of the area_of_interest30\(Area_{area} = 724.19\) as follows:
The calculated area_terms match, albeit with what appears to be some minor rounding errors. This appears to related to the ABS reported square_km (724.1681) being slightly different from the m2 reported by the st_area function used within the package (724,272,059). Likely this is related to the ABS having a slightly different methodology for calculating the area, and the error is approximately 0.015%.
SI values for Mornington Penninsula Railway services on 30/12/2018 (full day)
area_id
SI
21402
0.0443966
si_by_area_and_hour_wider <- hourly(list_gtfs, stops_in_or_near_areas, "2018-12-30")
si_by_area_and_hour_wider %>% adorn_totals(where = "col") %>% kable(caption = "Mornington Penninsula Tourist Railway hourly SI values for December 30, 2018, for SA1 zones")
Mornington Penninsula Tourist Railway hourly SI values for December 30, 2018, for SA1 zones
area_id
10
11
12
13
14
15
Total
21402
0.0055496
0.0055496
0.0110991
0.0027748
0.0083244
0.0110991
0.0443966
Again, the results largely match31 Accounting again for the minor difference between the ABS and function-calculated areas of the Mornington Peninsula SA3.
A larger test: the Victorian GTFS and SA1 zones within the Melbourne CBD - North SA2 zone
The north of the Melbourne CBD was selected for testing the code as it is may be familiar to readers, and has three different transit modes. The SA1 and SA2 zone boundaries are shown below.
SA1 zone boundaries within the Melbourne CBD - North SA2, Source: ABS
Trains stop at Melbourne Central and Flagstaff stations. Trams run along Elizabeth, Swanston, William, La Trobe and other streets. There are also bus services throughout the area, including along Queen Street.
First step is to load the PTV GTFS data. Some data wrangling is required as there are duplicate stop_ids in the stop definitions.
##-----THIS CHUNK ONLY NEEDS TO BE RUN ONCE TO CREATE THE gtfs_duplicate_stops_removed.zip file-----HENCE EVAL = FALSE ABOVE
ptv_231228 <- tidytransit::read_gtfs("data/ptv_231228/gtfs.zip")
# This results in "Warning: Duplicated ids found in: stops The returned object is not a tidygtfs object, you can use as_tidygtfs() after fixing the issue."
#So, remove the duplicated stops
#identify duplicate stops
ptv_231228_duplicated_stops <- tabyl(ptv_231228$stops$stop_id) %>% filter (n>1)
names(ptv_231228_duplicated_stops) <- c("stop_id", "n", "percent")
ptv_231228_duplicated_stops <- left_join(ptv_231228_duplicated_stops, ptv_231228$stops)
##discard duplicates
ptv_231228$stops <- ptv_231228$stops[!duplicated(ptv_231228$stops$stop_id),]
## Write gtfs back to file
ptv_231228 <- as_tidygtfs(ptv_231228)
tidytransit::write_gtfs(ptv_231228, "data/ptv_231228/gtfs_duplicate_stops_removed.zip")
Now that the duplicate stop_ids have been removed and the gtfs feed written back to file (“data/ptv_231228/gtfs_duplicate_stops_removed.zip”), the gtfs_by_route_type function can be run.
## convert to list of tidygtfs objects
ptv_231228_list_gtfs <- gtfssupplyindex::gtfs_by_route_type("data/ptv_231228/gtfs_duplicate_stops_removed.zip")
Trams only
Looking first at the trams only, running the stops_in_walk_dist function with verbose = TRUE gives the following output
Catchment of each stop within each area of interest, Melbourne CBD - North SA1 zones, trams
Catchment of each stop within each area of interest, Melbourne CBD - North SA1 zones, trams, by stop, first six stops
In general, the results appear to meet expectations. The first stop has a stop id of 17591. This stop is located at Russell and Bourke Street32https://transitfeeds.com/p/ptv/497/latest/stops?q=17591. A 400 metres catchment overlaid on this stop is shown in the margin33 Note: Roughly drawn from the ABS maps.. This matches the code output shown above (top left of figure).
SA1 zone boundaries within the Melbourne CBD - North SA2, overlayed with 400m radius from Russell and Bourke Street. Source: ABS and author
Running the hourly function for January 16, 2024 gives the following output:
Victorian Trams and SA1 zones within the Melbourne CBD - North SA2 zone, hourly SI values for January 16, 2024, first 6 SA1 zones, zeroed columns excluded.
area_id
5:00
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
22:00
23:00
24:00
25:00
20604150401
38
244
354
434
400
367
351
352
363
361
369
411
432
362
279
222
189
164
166
69
2
20604150402
8
59
84
106
110
110
103
103
104
104
107
117
107
95
74
56
48
40
42
15
1
20604150403
8
41
57
70
70
74
68
66
68
67
73
78
66
64
52
43
35
29
30
13
2
20604150404
27
185
267
331
304
280
266
267
276
273
281
313
331
278
215
171
143
125
125
55
2
20604150405
20
129
186
231
222
208
198
199
202
201
205
231
230
197
153
117
100
84
88
37
2
20604150406
29
176
252
312
293
270
258
259
266
263
272
304
312
263
205
158
130
115
117
49
2
This appears to meet expectations, with SI scores peaking in the morning (8-9) and evening (17-18).
The below figure maps this output.
Victorian Trams and SA1 zones within the Melbourne CBD - North SA2 zone,SI values for January 16, 2024, by hour
In general, the results match expectations, with higher SI scores for SA1 zones that area closer to the Swanston, Elizabeth and Latrobe St corridors (which are where the trams are!)
Rail only
Looking next at the rail only, running the stops_in_walk_dist function with verbose = TRUE gives the following output
Catchment of each stop within each area of interest, Melbourne CBD - North SA1 zones, rail
Catchment of each stop within each area of interest, Melbourne CBD - North SA1 zones, rail, by stop, first six stops
In general, the results appear to meet expectations:
Flagstaff Railway Station (stop_id 19841) is roughly 800m from the intersection of Swanston Street and Lonsdale Street, which is where the arc shown in the bottom right of the top left figure terminates34https://transitfeeds.com/p/ptv/497/latest/stop/19841;
The entire SA2 is within the 800m catchment of Melbourne Central Station (stop_id 19842);
Only a small part of the SA2 is within 800m of Parliament Station (to the south east, stop_id 19843).
Running the hourly function for January 16, 2024 gives the following output:
Victorian rail and SA1 zones within the Melbourne CBD - North SA2 zone, hourly SI values for January 16, 2024, first 75 SA1 zones, zeroed columns excluded.
area_id
5:00
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
22:00
23:00
24:00
25:00
20604150401
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150402
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150403
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150404
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150405
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150406
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150407
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150408
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150409
87
126
219
257
199
106
97
85
94
108
151
181
202
162
90
63
53
39
36
18
0
20604150410
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150411
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150412
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150413
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150414
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150415
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150416
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150417
98
146
252
298
230
125
114
102
110
125
173
208
232
188
107
75
62
47
44
22
0
20604150418
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150419
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150420
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150421
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150422
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150423
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150424
79
116
201
236
183
97
89
78
86
99
139
166
186
149
82
58
48
35
33
17
0
20604150425
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150426
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150427
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150428
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150429
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150430
95
152
257
303
236
126
117
103
113
130
183
216
243
195
108
79
65
47
45
23
0
20604150431
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150432
84
134
227
266
209
109
102
89
99
115
163
191
216
172
94
69
57
41
38
20
0
20604150433
83
133
225
264
207
108
101
88
98
114
162
190
214
171
93
68
56
40
38
20
0
20604150434
63
97
167
194
153
80
75
65
71
82
115
136
154
122
67
49
40
29
27
14
0
This appears to meet expectations, with SI scores peaking in the morning (8-9) and evening (17-18). A lot of the SA1 zones have identical SI scores because ALL of their area is within the 800m buffer distance of a railway station, meaning that the area terms cancel out and are equal to one.
The below figure maps this output.
Victorian rail and SA1 zones within the Melbourne CBD - North SA2 zone,SI values for January 16, 2024, by hour
In general, the SI scores appear to match expectations. SA1 zones closer to Melbourne Central and Flagstaff have higher scores.
Full SI calculation
This section runs the SI calculation for all modes (lrt, rail, bus) on SA1 zones within the Melbourne CBD - North SA2 zone. Here verbose is set as FALSE to cut down on the amount of output displayed.
Victorian GTFS and SA1 zones within the Melbourne CBD - North SA2 zone, hourly SI values for January 16, 2024, first 75 SA1 zones, zeroed columns excluded.
area_id
2:00
3:00
4:00
5:00
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
22:00
20604150401
0
0
2
123
389
603
729
636
498
474
463
484
498
554
627
673
558
396
307
258
214
20604150402
0
0
2
122
312
542
711
623
425
393
382
393
413
474
559
610
503
366
286
229
177
20604150403
0
0
2
96
216
361
435
375
256
241
227
238
254
309
357
377
319
221
170
138
108
20604150404
0
0
2
111
328
510
618
532
406
383
373
391
405
460
522
566
467
326
252
208
173
20604150405
0
0
2
138
395
672
855
749
543
506
496
511
533
599
700
763
634
452
356
287
224
20604150406
0
0
2
118
351
556
677
597
451
430
418
435
450
508
583
624
519
369
282
230
190
20604150407
0
0
2
125
326
536
669
591
421
400
388
401
420
485
576
631
511
364
278
224
176
20604150408
0
0
2
118
369
572
687
598
464
441
430
449
463
519
588
636
525
370
287
239
199
20604150409
0
0
4
164
426
715
886
752
575
542
527
548
572
630
729
806
660
466
374
309
246
20604150410
0
0
3
167
475
803
1034
905
664
624
608
631
653
725
852
941
777
547
431
350
274
20604150411
0
0
2
135
397
657
830
725
533
500
490
507
527
590
685
745
617
440
346
281
224
20604150412
0
0
2
129
422
654
784
686
542
517
506
529
544
600
683
733
606
429
337
285
235
20604150413
0
0
2
161
494
829
1056
914
683
641
628
652
673
744
866
958
789
563
450
364
288
20604150414
0
0
2
143
432
712
885
760
573
541
527
552
571
634
738
808
661
464
373
304
243
20604150415
0
0
2
114
272
474
624
541
355
328
316
327
345
403
473
513
427
311
245
195
150
20604150416
0
0
2
149
462
775
993
852
632
589
577
600
619
684
797
872
725
518
417
338
268
20604150417
0
0
5
191
518
862
1103
938
711
668
653
675
696
760
868
947
793
569
450
372
298
20604150418
0
0
2
127
408
634
765
668
525
500
490
511
527
582
659
705
587
416
325
275
227
20604150419
0
0
2
94
207
342
411
351
238
225
211
222
238
290
333
350
299
204
155
126
99
20604150420
0
0
2
124
388
609
745
649
502
478
467
487
502
558
634
678
566
401
310
261
214
20604150421
0
0
2
137
429
696
877
773
578
545
534
552
571
631
725
782
654
473
370
301
245
20604150422
0
0
2
140
431
688
842
723
558
531
516
543
559
617
715
777
636
444
355
297
241
20604150423
0
0
3
151
422
720
922
811
589
550
538
555
578
646
752
821
686
488
382
309
241
20604150424
0
0
2
133
393
643
774
661
518
488
475
497
513
562
649
703
580
410
334
278
224
20604150425
0
0
2
125
320
549
712
618
427
397
387
398
418
481
568
623
512
367
287
229
177
20604150426
0
0
2
142
440
716
893
773
588
555
543
567
586
647
749
814
671
474
375
308
248
20604150427
0
0
2
117
355
553
659
571
440
418
407
427
441
496
563
610
503
350
274
229
190
20604150428
0
0
3
113
309
478
581
496
370
349
336
354
367
426
483
524
437
305
234
191
158
20604150429
0
0
2
121
370
584
709
624
477
455
444
462
477
533
610
654
543
387
296
246
203
20604150430
0
0
3
179
533
874
1094
946
720
680
665
691
714
788
910
1004
825
585
468
383
306
20604150431
0
0
3
105
251
395
488
420
295
277
264
277
292
348
402
430
365
252
186
151
121
20604150432
0
0
4
180
506
854
1098
963
716
672
658
679
702
778
903
996
827
593
465
379
296
20604150433
0
0
3
168
480
814
1040
912
671
632
616
639
661
738
866
963
791
560
441
356
278
20604150434
0
0
1
104
355
548
644
565
459
438
428
447
457
497
566
608
502
357
286
244
203
This appears to meet expectations, with SI scores peaking in the morning (8-9) and evening (17-18).
The below figure maps this output.
Victorian rail and SA1 zones within the Melbourne CBD - North SA2 zone,SI values for January 16, 2024, by hour
In general, the SI scores appear to match expectations. SA1 zones closer to Melbourne Central and Flagstaff have higher scores.
Melbourne, Victoria
This section presents hourly SI values for all SA1 zones in Greater Melbourne for a typical weekday and weekend. The selected days are Tuesday 10th, Saturday 14th and Sunday 15th of October 2023. These days were selected so as to match the typical timing of the Australian Census (a Tuesday in early October), even though 2023 is not a census year.
##-----THIS CHUNK ONLY NEEDS TO BE RUN ONCE TO CREATE THE gtfs_duplicate_stops_removed.zip file-----HENCE EVAL = FALSE ABOVE
ptv_231006 <- tidytransit::read_gtfs("data/ptv_231006/gtfs.zip")
# This results in "Warning: Duplicated ids found in: stops The returned object is not a tidygtfs object, you can use as_tidygtfs() after fixing the issue."
#So, remove the duplicated stops
#identify duplicate stops
ptv_231006_duplicated_stops <- tabyl(ptv_231006$stops$stop_id) %>% filter (n>1)
names(ptv_231006_duplicated_stops) <- c("stop_id", "n", "percent")
ptv_231006_duplicated_stops <- left_join(ptv_231006_duplicated_stops, ptv_231006$stops)
##discard duplicates
ptv_231006$stops <- ptv_231006$stops[!duplicated(ptv_231006$stops$stop_id),]
## Write gtfs back to file
ptv_231006 <- as_tidygtfs(ptv_231006)
tidytransit::write_gtfs(ptv_231006, "data/ptv_231006/gtfs_duplicate_stops_removed.zip")
Again, there are duplicate stop_ids in the PTV GTFS dataset. These have been removed and the gtfs feed written back to file (“data/ptv_231006/gtfs_duplicate_stops_removed.zip”), on which the gtfs_by_route_type function can be run.
## convert to list of tidygtfs objects
ptv_231006_list_gtfs <- gtfssupplyindex::gtfs_by_route_type("data/ptv_231006/gtfs_duplicate_stops_removed.zip")
Tuesday 10th October, 2023
Victorian GTFS and SA1 zones within the Greater Melbourne GCCSA, hourly SI values for October 10, 2023, first 75 SA1 zones, 4am to 2am only
area_id
4:00
5:00
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
22:00
23:00
24:00
25:00
26:00
20601110601
0
10
27
45
51
51
43
39
38
43
38
47
51
51
51
32
23
18
8
5
3
0
0
20601110605
2
20
86
99
93
85
73
69
65
67
75
88
103
100
86
65
44
38
29
27
18
4
0
20601110606
1
19
69
84
84
73
68
62
60
61
67
75
90
85
70
56
35
29
19
18
12
2
0
20601110608
0
11
24
36
39
36
32
29
29
30
30
34
39
38
33
25
17
13
9
8
5
0
0
20601110609
2
26
64
90
90
89
74
69
66
69
72
82
97
97
85
68
50
41
29
25
16
3
0
20601110610
4
40
99
137
131
133
108
102
95
99
106
121
146
150
130
107
82
66
50
43
29
7
0
20601110611
1
21
47
71
77
78
64
60
57
61
60
70
79
80
75
56
41
33
20
16
10
1
0
20601110612
2
22
67
93
95
97
79
74
69
76
75
88
101
104
95
69
51
40
27
22
15
3
0
20601110613
2
19
61
79
76
76
63
60
53
61
61
71
81
84
77
51
37
29
24
22
15
4
0
20601110614
3
21
67
85
78
81
66
65
57
63
65
76
87
89
81
57
41
32
27
27
18
5
0
20601110615
0
2
16
25
26
26
22
20
18
24
18
25
26
26
28
10
5
4
2
1
1
0
0
20601110616
0
17
49
64
67
56
56
49
49
50
53
59
69
64
53
42
25
21
13
13
8
0
0
20601110617
2
15
58
70
62
68
56
56
52
55
56
65
67
68
64
42
33
21
14
14
10
4
0
20601110618
2
20
64
88
89
90
73
70
64
72
70
83
94
97
89
61
45
35
24
20
14
3
0
20601110619
2
24
64
91
93
94
77
72
68
73
73
85
99
101
92
70
52
42
28
23
15
3
0
20601110620
3
34
98
120
115
117
104
100
95
97
101
113
124
127
119
97
80
60
44
40
19
5
0
20601110621
0
31
68
91
94
93
87
84
84
84
84
88
95
94
92
76
60
44
30
27
9
0
0
20601110622
0
29
67
90
95
86
85
79
78
79
79
85
95
91
84
70
49
38
25
23
9
0
0
20601110623
2
26
77
96
93
91
80
76
73
74
79
88
99
99
91
73
55
43
32
29
16
3
0
20601110624
0
32
71
94
97
96
89
86
86
86
87
91
98
97
94
78
61
45
31
28
10
1
0
20601110625
2
29
80
101
97
99
87
84
80
81
85
95
103
105
100
81
66
50
37
33
16
4
0
20601110626
3
26
99
118
108
99
86
81
76
80
87
102
120
119
100
78
53
45
35
32
21
5
0
20601110627
0
20
63
80
84
69
72
62
63
62
66
74
87
80
65
53
31
26
14
14
8
0
0
20601110628
3
27
96
114
108
103
86
82
76
79
87
103
118
119
104
79
58
47
38
35
23
6
0
20601110629
2
23
84
102
96
86
77
71
67
71
76
88
106
103
87
68
46
39
30
27
18
4
0
20601110630
3
30
99
123
112
107
91
86
79
84
91
105
126
127
108
85
59
50
41
37
25
6
0
20601110701
0
27
91
107
117
89
84
76
76
76
82
93
110
111
104
71
61
37
18
17
13
0
0
20601110702
0
19
64
75
85
70
66
58
59
59
62
67
80
83
76
54
47
30
14
12
9
0
0
20601110703
0
13
53
60
61
48
45
41
41
41
45
54
61
59
54
34
31
16
8
8
5
0
0
20601110704
0
19
67
76
82
66
62
54
55
55
59
68
78
81
75
52
45
27
16
14
10
0
0
20601110705
0
14
36
42
51
47
44
37
38
38
38
37
45
52
50
37
31
23
12
10
8
0
0
20601110706
0
13
35
41
45
42
39
32
34
34
33
37
41
48
46
33
27
20
13
10
8
0
0
20601110707
0
9
32
42
44
43
38
32
39
32
34
37
40
44
47
23
14
11
7
7
7
0
0
20601110708
0
22
56
70
70
66
58
49
57
50
53
59
64
73
78
45
30
24
17
16
16
0
0
20601110710
0
11
33
41
41
42
37
32
36
32
32
38
38
43
44
26
16
13
9
8
8
0
0
20601110712
0
2
36
56
45
45
43
41
44
41
43
44
50
44
47
21
19
7
1
1
1
0
0
20601110713
0
16
67
93
82
78
72
69
69
69
71
73
84
78
91
49
42
22
13
11
11
0
0
20601110714
0
18
54
71
69
62
55
49
55
49
53
60
65
69
76
40
28
19
14
13
13
0
0
20601110715
0
0
14
21
21
22
19
18
19
15
17
22
21
21
21
8
0
0
0
0
0
0
0
20601110716
0
2
42
61
53
61
51
51
51
51
51
52
52
53
51
34
24
8
0
0
0
0
0
20601110717
0
17
46
59
59
54
48
43
47
42
45
52
55
59
64
36
24
17
13
12
12
0
0
20601110718
0
0
29
41
36
41
35
35
35
35
35
36
36
35
36
23
16
6
0
0
0
0
0
20601110719
0
2
29
40
37
41
35
34
35
32
34
37
38
36
38
21
14
5
2
1
1
0
0
20601110720
1
24
49
65
59
55
48
47
46
47
48
51
56
64
72
48
44
33
18
17
13
0
0
20601110721
0
25
51
69
64
55
49
47
47
47
50
54
58
68
75
49
42
32
18
18
14
0
0
20601110722
0
1
15
18
14
17
14
18
14
17
14
17
18
19
19
14
14
12
3
0
0
0
0
20601110723
0
7
20
26
24
22
21
21
20
21
21
22
23
26
28
21
19
15
5
5
4
0
0
20601110724
0
29
72
92
91
67
62
58
58
58
65
74
82
89
88
56
46
31
18
21
15
0
0
20601110725
0
10
39
44
44
34
32
30
30
30
33
40
44
42
39
23
22
11
5
6
4
0
0
20601110726
0
28
92
109
113
83
79
73
73
73
80
92
108
106
100
65
55
33
17
19
13
0
0
20601110727
0
0
23
34
31
34
29
29
29
26
28
31
31
31
31
16
7
2
0
0
0
0
0
20601110728
0
19
67
89
82
79
71
66
68
66
69
73
82
78
91
52
42
24
15
13
13
0
0
20601110729
0
21
61
75
75
56
53
49
49
49
53
62
70
72
71
45
37
24
13
15
11
0
0
20601110730
0
26
89
103
104
81
76
68
69
69
75
89
101
100
95
60
52
30
16
17
12
0
0
20601110731
0
1
15
18
16
16
14
17
14
17
15
17
18
18
18
14
13
10
2
0
0
0
0
20601110732
0
9
31
39
39
38
34
30
34
29
30
37
37
40
42
22
12
10
7
7
7
0
0
20601110733
0
24
59
75
74
69
61
53
60
53
56
64
67
77
82
48
31
25
18
17
17
0
0
20601110734
0
0
12
17
17
20
17
16
18
14
15
20
17
17
17
6
0
0
0
0
0
0
0
20601110735
0
0
12
17
18
20
17
16
18
14
15
20
18
18
18
7
1
1
1
0
0
0
0
20601110736
0
20
51
63
62
61
54
45
52
47
48
55
57
66
69
42
28
23
16
15
14
0
0
20601110901
0
27
65
88
88
79
74
72
70
70
74
77
79
91
93
63
50
34
25
19
15
0
0
20601110902
0
19
37
50
50
43
41
39
38
38
40
42
44
52
54
35
28
19
15
13
11
0
0
20601110903
0
27
59
65
66
62
55
55
53
53
56
59
63
67
73
56
52
31
22
19
10
0
0
20601110904
0
12
25
60
60
54
55
52
52
52
53
52
50
63
59
25
15
11
10
8
8
0
0
20601110905
0
24
51
105
103
92
92
87
87
87
90
86
86
108
103
47
30
22
19
17
17
0
0
20601110906
0
18
61
82
74
73
67
63
63
63
65
66
75
73
85
48
40
22
14
12
12
0
0
20601110907
0
19
38
79
78
69
69
65
65
65
67
65
65
82
79
35
24
17
15
13
13
0
0
20601110908
0
4
26
41
37
40
36
35
35
35
35
35
36
37
38
22
16
7
3
2
2
0
0
20601110909
0
2
35
49
42
48
42
41
41
41
41
41
44
40
44
28
22
8
2
1
1
0
0
20601110910
0
0
27
39
33
38
33
33
33
33
33
33
33
33
33
21
15
5
0
0
0
0
0
20601110911
0
4
9
40
40
38
38
38
38
38
38
36
33
41
36
11
4
3
3
2
2
0
0
20601110912
0
13
26
27
28
29
24
25
24
24
25
28
27
27
30
23
25
14
10
8
2
0
0
20601110913
1
8
48
68
64
67
51
51
51
50
49
54
57
60
57
38
26
10
6
5
2
0
0
20601110914
0
2
33
46
41
46
38
38
38
38
38
39
39
40
40
26
19
7
1
1
1
0
0
20601110915
0
4
7
15
15
15
13
14
13
13
14
13
13
14
14
7
6
3
3
3
1
0
0
This appears to meet expectations, with SI scores peaking in the morning (8-9) and evening (17-18). The below figure maps this output for just the SA1 zones within the Melbourne CBD - North SA2 zone.
Victorian rail and SA1 zones within the Melbourne CBD - North SA2 zone,SI values for January 16, 2024, by hour between 5am and 1am
Victorian rail and SA1 zones within the Melbourne CBD - North SA2 zone,SI values for January 16, 2024, by hour between 5am and 1am
Victorian rail and SA1 zones within the Melbourne CBD - North SA2 zone,SI values for January 16, 2024, by hour between 5am and 1am
Saturday 14th October, 2023
Victorian GTFS and SA1 zones within the Greater Melbourne GCCSA, hourly SI values for October 10, 2023, first 75 SA1 zones, 4am to 2am only
area_id
4:00
5:00
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
22:00
23:00
24:00
25:00
26:00
20601110601
2
4
17
22
28
29
28
28
30
30
30
30
30
26
28
20
14
12
11
8
5
5
3
20601110605
2
18
42
45
52
62
57
64
65
68
58
58
58
58
54
39
34
28
27
27
27
18
7
20601110606
5
14
30
37
44
52
46
55
55
59
49
49
48
47
42
28
24
19
18
18
18
13
7
20601110608
4
7
12
16
20
22
20
22
24
25
23
23
21
21
19
16
11
10
9
9
8
7
5
20601110609
4
18
39
44
50
56
53
58
58
59
57
57
56
56
54
48
37
32
30
27
25
17
8
20601110610
4
29
64
71
77
87
83
90
88
88
88
88
87
89
85
80
65
54
51
47
44
29
11
20601110611
4
12
29
36
43
44
44
45
47
47
47
47
46
44
44
40
28
25
23
19
16
12
7
20601110612
2
15
39
52
56
62
59
61
61
61
61
61
61
57
59
49
37
31
29
26
23
16
6
20601110613
2
16
33
48
48
55
52
52
52
52
52
52
52
46
50
37
31
25
23
23
23
15
6
20601110614
2
19
36
51
51
57
56
56
56
56
54
56
55
51
54
41
37
29
26
26
27
18
7
20601110615
0
1
6
17
16
21
16
16
16
16
16
17
16
11
16
5
4
3
2
2
1
1
1
20601110616
6
11
19
28
33
38
34
41
43
45
37
37
35
35
30
21
16
14
13
13
13
10
8
20601110617
1
10
18
36
41
37
43
43
43
46
38
48
36
40
43
30
35
20
14
14
14
10
4
20601110618
2
14
36
51
54
60
57
57
58
58
58
58
58
52
56
44
33
28
26
24
21
14
6
20601110619
3
16
39
48
54
58
57
59
59
59
59
59
59
57
57
51
38
32
30
27
23
16
7
20601110620
8
25
43
66
68
84
77
82
77
86
78
85
77
85
75
77
57
56
41
40
35
24
12
20601110621
12
17
24
47
50
64
56
62
61
67
60
65
58
61
55
55
36
38
28
27
21
18
13
20601110622
11
15
24
41
47
60
52
61
61
63
56
58
54
54
49
42
29
28
23
23
18
16
13
20601110623
6
19
36
48
52
64
59
65
62
67
60
63
60
62
56
52
40
37
30
29
27
19
10
20601110624
12
18
25
48
51
66
58
65
63
69
62
66
60
62
57
56
38
39
29
28
22
18
14
20601110625
7
21
36
54
55
71
64
69
65
72
65
71
64
70
62
64
47
47
34
33
29
21
11
20601110626
3
22
50
55
61
74
66
76
76
79
69
69
68
70
65
46
41
35
32
32
32
21
8
20601110627
7
11
22
33
41
50
42
53
54
57
46
46
44
43
37
24
18
14
14
14
13
11
9
20601110628
3
24
52
56
62
74
70
75
74
78
70
72
70
72
66
56
48
40
35
35
35
23
9
20601110629
3
19
42
47
53
64
56
66
66
69
60
60
58
60
56
40
35
30
28
27
27
18
7
20601110630
3
24
54
60
64
79
70
80
79
81
74
74
73
75
71
54
48
41
38
38
37
25
10
20601110701
0
15
42
46
56
61
59
71
71
71
66
61
61
61
56
42
27
23
20
18
15
14
4
20601110702
0
10
31
34
40
46
45
53
53
53
50
45
45
45
42
36
20
18
17
13
10
10
3
20601110703
0
6
21
24
30
34
31
40
40
40
36
32
32
32
28
17
10
9
8
7
6
6
0
20601110704
0
12
32
33
41
44
43
51
51
51
48
45
45
45
41
34
20
18
16
14
12
12
1
20601110705
0
9
22
22
25
30
31
33
33
33
33
29
29
29
29
31
17
16
14
11
9
9
3
20601110706
0
9
21
20
23
26
28
30
30
30
31
27
27
27
26
28
16
14
13
11
9
9
1
20601110707
0
7
16
24
32
29
36
32
31
31
32
24
24
25
24
19
10
9
7
7
7
7
1
20601110708
0
16
33
37
49
45
53
51
49
49
50
42
42
43
42
37
22
19
17
16
16
16
3
20601110710
0
8
18
23
29
27
34
32
31
31
33
25
25
24
25
20
12
10
9
9
8
8
1
20601110712
0
1
3
24
30
27
26
30
30
30
31
26
20
29
29
15
19
13
1
1
1
1
0
20601110713
0
11
21
41
47
51
46
53
53
54
54
46
47
51
52
39
38
28
11
11
11
11
2
20601110714
0
13
27
37
49
42
49
46
45
45
46
39
40
41
41
33
22
18
13
13
13
13
2
20601110715
0
0
3
17
18
14
18
15
15
15
16
14
10
11
10
4
0
0
0
0
0
0
0
20601110716
0
0
1
28
27
35
28
39
35
35
30
27
32
25
24
24
24
16
0
0
0
0
0
20601110717
0
12
24
33
41
37
43
41
39
39
41
34
36
35
35
30
18
15
12
12
12
12
2
20601110718
0
0
0
18
19
24
18
24
24
24
24
18
23
18
18
17
17
11
0
0
0
0
0
20601110719
0
1
5
22
23
24
24
25
25
25
26
20
23
19
19
15
13
9
1
1
1
1
0
20601110720
1
18
24
35
38
41
40
41
42
44
41
44
41
44
39
43
34
31
20
16
16
10
3
20601110721
0
19
26
34
39
40
41
42
43
45
43
44
42
44
39
42
33
29
19
16
17
11
3
20601110722
0
0
1
8
12
11
10
11
12
11
12
13
12
11
11
11
12
11
5
0
0
0
0
20601110723
0
5
7
13
16
16
17
17
17
17
17
18
17
17
17
17
15
14
7
4
5
3
1
20601110724
0
20
36
39
48
49
49
57
58
58
54
51
51
51
45
36
28
22
18
18
19
14
3
20601110725
0
5
15
17
22
25
22
29
29
29
26
23
23
23
20
10
7
6
5
5
5
4
0
20601110726
0
17
41
44
56
60
57
71
71
71
65
59
59
59
53
35
25
20
18
17
16
14
3
20601110727
0
0
3
21
21
21
21
21
21
21
21
19
18
16
15
10
7
5
0
0
0
0
0
20601110728
0
13
24
41
48
52
48
54
53
54
54
46
52
48
49
43
37
27
13
13
13
13
2
20601110729
0
14
29
31
39
40
39
48
48
48
45
41
41
41
37
26
20
17
13
13
13
11
2
20601110730
0
15
40
42
53
57
53
68
68
68
62
56
56
56
50
32
22
19
16
15
15
14
1
20601110731
0
0
2
7
11
11
11
12
12
12
12
12
12
11
11
10
10
9
4
0
0
0
0
20601110732
0
7
16
24
30
25
33
30
29
28
31
24
23
23
23
17
9
8
7
7
7
7
1
20601110733
0
17
35
40
52
47
57
54
52
52
55
45
45
45
45
39
23
20
17
17
17
17
3
20601110734
0
0
3
14
16
12
18
14
14
14
16
11
9
9
9
3
0
0
0
0
0
0
0
20601110735
0
0
3
13
15
12
17
14
14
14
16
11
9
9
9
4
1
1
1
0
0
0
0
20601110736
0
15
30
33
43
39
48
47
46
45
48
38
38
38
38
34
20
18
15
15
15
15
2
20601110901
0
14
31
43
51
45
53
51
54
51
45
45
46
44
41
40
36
28
20
18
15
14
3
20601110902
0
10
21
23
28
24
30
27
31
28
26
24
26
24
23
25
21
16
13
13
11
10
2
20601110903
0
7
28
34
38
32
38
36
38
34
36
34
40
34
34
34
33
25
19
17
9
7
2
20601110904
0
8
14
20
28
28
37
30
37
31
19
17
17
17
15
17
13
10
8
8
8
8
1
20601110905
0
17
28
37
50
52
61
55
62
58
35
33
33
33
30
33
25
19
17
17
17
17
3
20601110906
0
12
20
37
41
47
41
47
49
49
48
42
46
43
43
38
36
26
12
12
12
12
2
20601110907
0
13
22
28
37
41
45
41
47
43
28
26
26
26
24
26
19
15
13
13
13
13
2
20601110908
0
2
4
17
18
23
20
23
24
23
21
16
20
16
16
16
15
11
2
2
2
2
0
20601110909
0
1
3
21
22
28
22
28
28
28
28
22
28
22
22
21
21
14
1
1
1
1
0
20601110910
0
0
0
16
16
22
16
22
22
22
21
16
21
15
16
15
16
10
0
0
0
0
0
20601110911
0
2
4
10
15
18
24
18
24
18
7
5
5
5
4
5
4
3
2
2
2
2
0
20601110912
0
0
12
11
14
11
14
11
14
10
12
10
15
10
12
11
15
10
8
6
2
0
0
20601110913
2
5
7
30
29
36
30
42
36
36
31
29
35
28
28
27
26
19
5
5
3
3
2
20601110914
1
1
2
20
20
26
20
28
26
26
23
20
26
19
20
19
19
13
1
1
1
1
1
20601110915
0
0
3
5
6
6
8
6
8
6
4
3
4
3
3
3
4
3
2
2
1
0
0
This appears to meet expectations, with SI scores peaking in the morning (8-9) and evening (17-18). The below figure maps this output for just the SA1 zones within the Melbourne CBD - North SA2 zone.
Victorian GTFS and SA1 zones within the Melbourne CBD - North SA2 zone,SI values for Saturday 14th October 2023, by hour between 5am and 1amr
Victorian GTFS and SA1 zones within the Clayton (North) - Notting Hill SA2 zone, SI values for Saturday 14th October 2023, by hour between 5am and 1am
Victorian GTFS and SA1 zones within the Caulfield - North SA2 zone, SI values for Saturday 14th October 2023, by hour between 5am and 1am
Sunday 15th October, 2023
Victorian GTFS and SA1 zones within the Greater Melbourne GCCSA, hourly SI values for October 10, 2023, first 75 SA1 zones, 4am to 2am only
area_id
4:00
5:00
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
22:00
23:00
24:00
25:00
26:00
20601110601
0
10
27
45
51
51
43
39
38
43
38
47
51
51
51
32
23
18
8
5
3
0
0
20601110605
2
20
86
99
93
85
73
69
65
67
75
88
103
100
86
65
44
38
29
27
18
4
0
20601110606
1
19
69
84
84
73
68
62
60
61
67
75
90
85
70
56
35
29
19
18
12
2
0
20601110608
0
11
24
36
39
36
32
29
29
30
30
34
39
38
33
25
17
13
9
8
5
0
0
20601110609
2
26
64
90
90
89
74
69
66
69
72
82
97
97
85
68
50
41
29
25
16
3
0
20601110610
4
40
99
137
131
133
108
102
95
99
106
121
146
150
130
107
82
66
50
43
29
7
0
20601110611
1
21
47
71
77
78
64
60
57
61
60
70
79
80
75
56
41
33
20
16
10
1
0
20601110612
2
22
67
93
95
97
79
74
69
76
75
88
101
104
95
69
51
40
27
22
15
3
0
20601110613
2
19
61
79
76
76
63
60
53
61
61
71
81
84
77
51
37
29
24
22
15
4
0
20601110614
3
21
67
85
78
81
66
65
57
63
65
76
87
89
81
57
41
32
27
27
18
5
0
20601110615
0
2
16
25
26
26
22
20
18
24
18
25
26
26
28
10
5
4
2
1
1
0
0
20601110616
0
17
49
64
67
56
56
49
49
50
53
59
69
64
53
42
25
21
13
13
8
0
0
20601110617
2
15
58
70
62
68
56
56
52
55
56
65
67
68
64
42
33
21
14
14
10
4
0
20601110618
2
20
64
88
89
90
73
70
64
72
70
83
94
97
89
61
45
35
24
20
14
3
0
20601110619
2
24
64
91
93
94
77
72
68
73
73
85
99
101
92
70
52
42
28
23
15
3
0
20601110620
3
34
98
120
115
117
104
100
95
97
101
113
124
127
119
97
80
60
44
40
19
5
0
20601110621
0
31
68
91
94
93
87
84
84
84
84
88
95
94
92
76
60
44
30
27
9
0
0
20601110622
0
29
67
90
95
86
85
79
78
79
79
85
95
91
84
70
49
38
25
23
9
0
0
20601110623
2
26
77
96
93
91
80
76
73
74
79
88
99
99
91
73
55
43
32
29
16
3
0
20601110624
0
32
71
94
97
96
89
86
86
86
87
91
98
97
94
78
61
45
31
28
10
1
0
20601110625
2
29
80
101
97
99
87
84
80
81
85
95
103
105
100
81
66
50
37
33
16
4
0
20601110626
3
26
99
118
108
99
86
81
76
80
87
102
120
119
100
78
53
45
35
32
21
5
0
20601110627
0
20
63
80
84
69
72
62
63
62
66
74
87
80
65
53
31
26
14
14
8
0
0
20601110628
3
27
96
114
108
103
86
82
76
79
87
103
118
119
104
79
58
47
38
35
23
6
0
20601110629
2
23
84
102
96
86
77
71
67
71
76
88
106
103
87
68
46
39
30
27
18
4
0
20601110630
3
30
99
123
112
107
91
86
79
84
91
105
126
127
108
85
59
50
41
37
25
6
0
20601110701
0
27
91
107
117
89
84
76
76
76
82
93
110
111
104
71
61
37
18
17
13
0
0
20601110702
0
19
64
75
85
70
66
58
59
59
62
67
80
83
76
54
47
30
14
12
9
0
0
20601110703
0
13
53
60
61
48
45
41
41
41
45
54
61
59
54
34
31
16
8
8
5
0
0
20601110704
0
19
67
76
82
66
62
54
55
55
59
68
78
81
75
52
45
27
16
14
10
0
0
20601110705
0
14
36
42
51
47
44
37
38
38
38
37
45
52
50
37
31
23
12
10
8
0
0
20601110706
0
13
35
41
45
42
39
32
34
34
33
37
41
48
46
33
27
20
13
10
8
0
0
20601110707
0
9
32
42
44
43
38
32
39
32
34
37
40
44
47
23
14
11
7
7
7
0
0
20601110708
0
22
56
70
70
66
58
49
57
50
53
59
64
73
78
45
30
24
17
16
16
0
0
20601110710
0
11
33
41
41
42
37
32
36
32
32
38
38
43
44
26
16
13
9
8
8
0
0
20601110712
0
2
36
56
45
45
43
41
44
41
43
44
50
44
47
21
19
7
1
1
1
0
0
20601110713
0
16
67
93
82
78
72
69
69
69
71
73
84
78
91
49
42
22
13
11
11
0
0
20601110714
0
18
54
71
69
62
55
49
55
49
53
60
65
69
76
40
28
19
14
13
13
0
0
20601110715
0
0
14
21
21
22
19
18
19
15
17
22
21
21
21
8
0
0
0
0
0
0
0
20601110716
0
2
42
61
53
61
51
51
51
51
51
52
52
53
51
34
24
8
0
0
0
0
0
20601110717
0
17
46
59
59
54
48
43
47
42
45
52
55
59
64
36
24
17
13
12
12
0
0
20601110718
0
0
29
41
36
41
35
35
35
35
35
36
36
35
36
23
16
6
0
0
0
0
0
20601110719
0
2
29
40
37
41
35
34
35
32
34
37
38
36
38
21
14
5
2
1
1
0
0
20601110720
1
24
49
65
59
55
48
47
46
47
48
51
56
64
72
48
44
33
18
17
13
0
0
20601110721
0
25
51
69
64
55
49
47
47
47
50
54
58
68
75
49
42
32
18
18
14
0
0
20601110722
0
1
15
18
14
17
14
18
14
17
14
17
18
19
19
14
14
12
3
0
0
0
0
20601110723
0
7
20
26
24
22
21
21
20
21
21
22
23
26
28
21
19
15
5
5
4
0
0
20601110724
0
29
72
92
91
67
62
58
58
58
65
74
82
89
88
56
46
31
18
21
15
0
0
20601110725
0
10
39
44
44
34
32
30
30
30
33
40
44
42
39
23
22
11
5
6
4
0
0
20601110726
0
28
92
109
113
83
79
73
73
73
80
92
108
106
100
65
55
33
17
19
13
0
0
20601110727
0
0
23
34
31
34
29
29
29
26
28
31
31
31
31
16
7
2
0
0
0
0
0
20601110728
0
19
67
89
82
79
71
66
68
66
69
73
82
78
91
52
42
24
15
13
13
0
0
20601110729
0
21
61
75
75
56
53
49
49
49
53
62
70
72
71
45
37
24
13
15
11
0
0
20601110730
0
26
89
103
104
81
76
68
69
69
75
89
101
100
95
60
52
30
16
17
12
0
0
20601110731
0
1
15
18
16
16
14
17
14
17
15
17
18
18
18
14
13
10
2
0
0
0
0
20601110732
0
9
31
39
39
38
34
30
34
29
30
37
37
40
42
22
12
10
7
7
7
0
0
20601110733
0
24
59
75
74
69
61
53
60
53
56
64
67
77
82
48
31
25
18
17
17
0
0
20601110734
0
0
12
17
17
20
17
16
18
14
15
20
17
17
17
6
0
0
0
0
0
0
0
20601110735
0
0
12
17
18
20
17
16
18
14
15
20
18
18
18
7
1
1
1
0
0
0
0
20601110736
0
20
51
63
62
61
54
45
52
47
48
55
57
66
69
42
28
23
16
15
14
0
0
20601110901
0
27
65
88
88
79
74
72
70
70
74
77
79
91
93
63
50
34
25
19
15
0
0
20601110902
0
19
37
50
50
43
41
39
38
38
40
42
44
52
54
35
28
19
15
13
11
0
0
20601110903
0
27
59
65
66
62
55
55
53
53
56
59
63
67
73
56
52
31
22
19
10
0
0
20601110904
0
12
25
60
60
54
55
52
52
52
53
52
50
63
59
25
15
11
10
8
8
0
0
20601110905
0
24
51
105
103
92
92
87
87
87
90
86
86
108
103
47
30
22
19
17
17
0
0
20601110906
0
18
61
82
74
73
67
63
63
63
65
66
75
73
85
48
40
22
14
12
12
0
0
20601110907
0
19
38
79
78
69
69
65
65
65
67
65
65
82
79
35
24
17
15
13
13
0
0
20601110908
0
4
26
41
37
40
36
35
35
35
35
35
36
37
38
22
16
7
3
2
2
0
0
20601110909
0
2
35
49
42
48
42
41
41
41
41
41
44
40
44
28
22
8
2
1
1
0
0
20601110910
0
0
27
39
33
38
33
33
33
33
33
33
33
33
33
21
15
5
0
0
0
0
0
20601110911
0
4
9
40
40
38
38
38
38
38
38
36
33
41
36
11
4
3
3
2
2
0
0
20601110912
0
13
26
27
28
29
24
25
24
24
25
28
27
27
30
23
25
14
10
8
2
0
0
20601110913
1
8
48
68
64
67
51
51
51
50
49
54
57
60
57
38
26
10
6
5
2
0
0
20601110914
0
2
33
46
41
46
38
38
38
38
38
39
39
40
40
26
19
7
1
1
1
0
0
20601110915
0
4
7
15
15
15
13
14
13
13
14
13
13
14
14
7
6
3
3
3
1
0
0
This appears to meet expectations, with SI scores peaking in the morning (8-9) and evening (17-18). The below figure maps this output for just the SA1 zones within the Melbourne CBD - North SA2 zone.
Victorian GTFS and SA1 zones within the Melbourne CBD - North SA2 zone,SI values for Sunday 15th October 2023, by hour between 5am and 1am
Victorian GTFS and SA1 zones within the MClayton (North) - Notting Hill SA2 zone,SI values for Sunday 15th October 2023, by hour between 5am and 1am
Victorian GTFS and SA1 zones within the Caulfield - North SA2 zone, SI values for Sunday 15th October 2023, by hour between 5am and 1am
Mornington Penninsula SA1 zones and location of Mornington Tourist Rail stops.
Entity Relationship Diagram (ERD) excerpt, gtfs_by_route_type function
Entity Relationship Diagram (ERD) excerpt, load_areas_of_interest function
Mornington Penninsula Tourist Railway catchments within the Mornington Penninsula SA3 zone
Mornington Penninsula Tourist Railway catchments within the Mornington Penninsula SA3 zone
SA1 zone boundaries within the Melbourne CBD - North SA2, overlayed with 400m radius from Russell and Bourke Street. Source: ABS and author
